pastas.solver.least_squares.LeastSquaresBase#

class pastas.solver.least_squares.LeastSquaresBase(name: str = 'solver', pcov: pandas.DataFrame | None = None, **kwargs)#

Base class for least squares solvers.

property pcor: pandas.DataFrame | None#

Property to obtain the parameter correlations from the covariance matrix.

Returns:

pcor – Pandas DataFrame with the correlations for the parameters. If pcov is None, returns None.

Return type:

pandas.DataFrame or None

Methods#

get_parameter_sample(→ pastas.typing.ArrayLike)

Obtain a parameter sets for monte carlo analyses.

prediction_interval(→ pandas.DataFrame)

Calculate the prediction interval for the simulation.

ci_simulation(→ pandas.DataFrame)

Calculate the confidence interval for the simulation.

ci_block_response(→ pandas.DataFrame)

Calculate the confidence interval for the block response.

ci_step_response(→ pandas.DataFrame)

Calculate the confidence interval for the step response.

ci_contribution(→ pandas.DataFrame)

Calculate the confidence interval for the contribution.

solve(→ tuple[bool, pandas.DataFrame])

Solve the optimization problem.

fit_report(→ str)

Report on the fit after a model is optimized.

to_dict(→ dict)

Convert solver to a dictionary.